A Mutual Information-Based Hybrid Feature Selection Method for Software Cost Estimation Using Feature Clustering

Liu, Qin; Shi, Shihai; Zhu, Hongming; Xiao, Jiakai

doi:10.1109/compsac.2014.99

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…7,14,20,37,40,50 In the ABE methods, the effort of a new project can be estimated based on the efforts of K similar projects utilizing an adaptation function. In the majority of the previous ABE methods, mean, 5,14,[36][37][38][39][40][41][42][43][44][45][46][47][49][50][51][52][53][54]56,57,64,68 median, 8,67 and inverse rank weighted mean 7,18,48,51,55,62,63,66 are the mostly used adaptation functions.…”

Section: Evaluation Structures In Abe Techniquesmentioning

confidence: 99%

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Shahpar

Bardsiri

2021

Concurrency and Computation

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Software development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy-based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global-local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well-known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

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Section: Evaluation Structures In Abe Techniquesmentioning

confidence: 99%

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Shahpar

Bardsiri

2021

Concurrency and Computation

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“…FS algorithms can be broadly divided into two categories: The filter and Wrapper based approaches. A good example of the filter approach is the Relief and Focus algorithms, the Relief algorithm ranks each feature in the data set by assigning weights while the Focus is always searching for the minimal set of features that may be useful in classification [1][2]11]. Correlation FS as discussed in [1] can be used to evaluate the predictive power of each feature and the degree of redundancy between them by selecting those subsets of features with low level of inter-correlation.…”

Section: Previous Workmentioning

confidence: 99%

On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images

Ibrahim¹

2017

IJMI

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Feature selection techniques to search for the relevant features that would have the greatest influence on the predictive accuracy have been modified and applied in this paper. Selection search iteratively evaluates a subset of the feature, then modifies the subset and evaluates if the new subset is an improvement over the previous. The performances of the developed models are tested with some classifiers based on the feature variables selected by the proposed approach and the effects of some important parameters on the overall classification accuracy are analysed. Experimental results showed that the proposed approach consistently improved the classification accuracy. The improved classification accuracies on the multi-fractal datasets are statistically significant when compared with the previous methods applied in our previous publications. The use of the feature selection search tool reduces the classification model complexity and produces a robust system with greater efficiency, and excellent results. The research results also prove that the number of growing trees and the threshold values could affect the classification accuracy.

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“…Furthermore, MI-based measures are slightly affected by the monotone transformation and classifier selection [ 6 ]. These advantages allow MI-based measures for broad application in the analysis of various types of problems, including computer-aided diagnosis [ 7 ], cyber intrusion detection [ 8 ], heart failure recognition [ 9 ], and software cost estimation [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Nonparametric Feature Selection Approach Based on Mutual Information Transfer Network

Fard

2022

Entropy

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The filter feature selection algorithm is habitually used as an effective way to reduce the computational cost of data analysis by selecting and implementing only a subset of original features into the study. Mutual information (MI) is a popular measurement adopted to quantify the dependence among features. MI-based greedy forward methods (MIGFMs) have been widely applied to escape from computational complexity and exhaustion of high-dimensional data. However, most MIGFMs are parametric methods that necessitate proper preset parameters and stopping criteria. Improper parameters may lead to ignorance of better results. This paper proposes a novel nonparametric feature selection method based on mutual information and mixed-integer linear programming (MILP). By forming a mutual information network, we transform the feature selection problem into a maximum flow problem, which can be solved with the Gurobi solver in a reasonable time. The proposed method attempts to prevent negligence on obtaining a superior feature subset while keeping the computational cost in an affordable range. Analytical comparison of the proposed method with six feature selection methods reveals significantly better results compared to MIGFMs, considering classification accuracy.

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A Mutual Information-Based Hybrid Feature Selection Method for Software Cost Estimation Using Feature Clustering

Cited by 5 publications

References 12 publications

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

On Feature Selection Methods for Accurate Classification and Analysis of Emphysema CT Images

A Novel Nonparametric Feature Selection Approach Based on Mutual Information Transfer Network

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